中国工业行业的生态效率

中国工业是资源消费和污染物排放的主要源头,解析工业各行业的生态效率可为中国产业结构调整和整体环境质量改善提供必要信息.在分析工业构成并构建工业系统与外部环境之间关系的基础上,选择能源消费和废水、固体废物、SO2、工业烟尘、粉尘等污染物的排放量作为研究的环境影响类别,界定了与这些环境影响相关的生态效率,估算了中国2007年的工业生态效率现状,结果为:能源效率615.5万元·ktce-1;废水和固废方面的生态效率分别为54.3万元·kt-1和1.9万元·t-1;工业SO2、烟尘和粉尘方面的生态效率分别为59.6、169.2和184.6万元·t-1.深入分析了39个工业部门的生态效率,结果表明,以上几种生态效率具有明显的行业不均衡性,表现为不同行业在不同类型生态效率中的工业排序明显不同;与此同时,对于同一种生态效率,不同工业行业的数值可相差几百倍到几万倍,表明通过优化工业结构将具有较大的提高工业生态效率的可能性.     

焦作市工业行业的生态效率

以工业城市焦作市为例,选择工业能源消费和污染物(包括废水、固体废物、SO2、烟尘等)排放作为环境负荷,定量分析工业分行业的生态效率及分行业特定环境负荷对总环境负荷的贡献率.结果表明,能源效率最高的行业是黑色金属矿采选业(FMM),为43.19万元·tce-1,贡献率为0.003%;废水排放相关环境效率最高的是交通运输设...     

四川省人为源大气污染物排放清单及特征

在收集四川省各城市人为污染源活动水平数据基础上,基于自下而上和自上而下结合的清单构建方法,选取排放因子并结合GIS技术,建立了该地区2015年1 km×1 km人为源大气污染物排放清单.结果表明,2015年四川省人为源SO_2、NO_x、CO、PM_(10)、PM_(2.5)、BC、OC、VOCs和NH_3排放量分别为444.9×10~3、820.0×10~3、3 773.1×10~3、1 371.6×10~3、537.5×10~3、28.7×10~3、53.1×10~3、923.6×10~3和988.0×10~3t.电厂和工业锅炉等燃煤排放贡献了95%以上的SO_2,移动源、化石燃料燃烧源和工艺过程源分别贡献了54%、23%和20%的NO_x,以钢铁和建材制造为主的工艺过程源分别贡献了20%的PM_(10)和34%的PM_(2.5),以道路扬尘为主的扬尘源分别贡献了60%的PM_(10)和35%的PM_(2.5),生物质燃烧分别贡献了33%的BC和51%的OC,以机械加工、建筑装饰、电子设备制造、印刷和家具等行业为主的溶剂使用源贡献了46%的VOCs,NH_3主要来自畜禽养殖和氮肥施用等农业部门排放,分别占总排放量的70%和25%.污染物空间分布结果显示,四川省各项大气污染物主要集中分布于人口最为密集,农业和工业均较为发达的四川盆地和攀枝花部分区域,其中,以成都、德阳和绵阳为代表的成都平原城市群为四川盆地内的主要排放高值区域.所建立的排放清单存在一定不确定性,后续研究中应针对活动水平数据获取的不足开展数据收集工作,加强排放贡献较大典型污染源的排放因子本地化研究工作,逐步完善四川省大气污染物排放清单,为四川省复合型大气污染研究和防治提供科学支撑.     

东莞市低碳路径下加速电气化对CO2和污染物协同减排影响

城市是能源消耗的中心，电气化可以整合城市能源结构，实现清洁能源高效利用，探究城市低碳路径下加速电气化的协同减排影响对实现城市减污降碳至关重要.基于长期能源替代规划模型(LEAP-DG),设置了基准情景、低碳情景和加速电气化情景等3类情景，评估电气化措施在不同电力结构下的减排潜力，量化重点部门的措施贡献，探讨广东省典型制造业城市东莞的协同减排效果.结果表明，电力结构优化促进了电气化措施的协同减排效果，低碳路径下加速电气化将进一步降低电力污染物排放强度，2050年，东莞市CO₂、 NO_x、 VOC和CO减排7.35×10⁶、 1.28×10⁴、 1.62×10⁴和8.13×10⁴ t, SO₂和PM_2.5消费侧减排量和生产侧增排量达到平衡.电气化渗透速率和电力结构优化协调发展是电气化措施实现减排效益的关键，工业和交通部门加速电气化将同时降低CO₂和大气污染物排放，交通部门得益于燃油车和电动车的高...     

Managing the nitrogen cycle to reduce greenhouse gas emissions from crop production and biofuel expansion

Public policies are promoting biofuels as an alternative to fossil fuel consumption in order to mitigate greenhouse gas (GHG) emissions. However, the mitigation benefit can be at least partially compromised by emissions occurring during feedstock production. One of the key sources of GHG emissions from biofuel feedstock production, as well as conventional crops, is soil nitrous oxide (N₂O), which is largely driven by nitrogen (N) management. Our objective was to determine how much GHG emissions could be reduced by encouraging alternative N management practices through application of nitrification inhibitors and a cap on N fertilization. We used the US Renewable Fuel Standards (RFS2) as the basis for a case study to evaluate technical and economic drivers influencing the N management mitigation strategies. We estimated soil N₂O emissions using the DayCent ecosystem model and applied the US Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASOMGHG) to project GHG emissions for the agricultural sector, as influenced by biofuel scenarios and N management options. Relative to the current RSF2 policy with no N management interventions, results show decreases in N₂O emissions ranging from 3 to 4 % for the agricultural sector (5.5–6.5 million metric tonnes CO₂?eq.?year^?1; 1 million metric tonnes is equivalent to a Teragram) in response to a cap that reduces N fertilizer application and even larger reductions with application of nitrification inhibitors, ranging from 9 to 10 % (15.5–16.6 million tonnes CO₂?eq.?year^?1). The results demonstrate that climate and energy policies promoting biofuel production could consider options to manage the N cycle with alternative fertilization practices for the agricultural sector and likely enhance the mitigation of GHG emissions associated with biofuels.     

2010年中国机动车CH4和N2O排放清单

中国大部分机动车温室气体排放研究都集中于CO₂排放,对于CH₄和N₂O等排放的研究鲜见. 以中国机动车污染防治年报(2011年)、中国汽车工业年鉴(2011年)、中国统计年鉴(2011年)以及交通运输部发布的相关信息和数据(2011年)等为基础,结合文献调研和2008─2010年对北京、广州等国内10余座典型城市的实地调查结果,获得2010年我国机动车活动水平及排放特征. 基于上述基础信息,解析得到按不同车型、燃料和车龄分布的机动车保有量、年均行驶里程及排放因子,建立2010年中国机动车CH₄和N₂O排放清单. 结果表明:2010年中国机动车CH₄和N₂O排放量分别为23.90×104和6.01×104t,折算成CO₂分别为501.99×104和1862.51×104t. 不确定性分析则显示,中国CH₄排放量在18.21×104～27.52×104t之间,N₂O排放量在4.32×104～7.62×104t之间. 在机动车中,汽车CH₄和N₂O排放量最大,分担率(某车型污染物排放量占机动车排放总量的比例)分别为77.99%和94.22%,而摩托车和农用车排放分担率较小. 在各类汽车中,CH₄排放主要来源于轻型汽油车和天然气出租车,二者的排放分担率分别为47.98%和23.42%;N₂O排放则主要源于轻型汽油车,其分担率为73.09%. 因此,轻型汽油车是削减机动车CH₄和N₂O排放的重点车型,同时天然气出租车也应作为控制CH₄排放的主要车型.      

经济结构调整的污染减排效应：以COD减排为例

经济结构调整是减少污染物排放的重要手段.为定量分析经济结构调整对污染减排的影响,将全国分为东部、东北、中部和西部4个子区域、工业分为39个行业,以化学需氧量(COD)为例,通过构建模型分析区域结构和行业结构调整对总量减排和排放强度的影响.结果表明,2000～2010年:1中国COD排放量由1445×104t降至1 238×104t,11 a间共排放14 950×104t.其中东部地区排放量最多,占总量的比重约为35.6%.2在工业COD排放量中,造纸及纸制品业排放量最大,占工业COD排放量的35.8%.3四大地区经济结构变化减少COD排放420×104t,使COD排放强度降低1.29%.4工业内部行业结构的变化减少COD排放533×104t,使COD排放强度降低3.1%.研究结果对指导中国经济结构调整、实现节能减排目标具有一定的参考价值.     

Clean Development Mechanism Potential and Challenges in Sub-Saharan Africa

Sub-Saharan Africa lags far behind other regions in terms of the implementation of Clean Development Mechanism (CDM) projects due to several reasons. One of the reasons is a general perception that, since the region contributes very little to global GHG emissions, it also offers few opportunities to reduce these emissions. Using a bottom-up approach, this study investigates the technical potential of reducing GHG emissions from the energy sector in Sub-Saharan Africa through the CDM. The study finds that sub-Saharan Africa could develop 3,227 CDM projects, including 361 programs of activities, which could reduce approximately 9.8 billion tons of GHG emissions during the CDM project cycles. The study also estimates that the realization of this CDM potential could significantly enhance sustainable development in the region as it would attract more than US200 billion in investment and could generate US200 billion in investment and could generate US98 billion of CDM revenue at a CER price of US$10/tCO2. Another notable finding of the study is that the realization of this CDM potential could supply clean electricity by doubling the current capacity and thereby providing access of electricity to millions of people in the region. However, realization of this CDM potential is severely constrained by a number of financial, technical, regulatory and institutional barriers.     

基于投入产出法的北京能源消耗温室气体排放清单分析

城市是一个巨大能源物资消耗体和温室气体排放体,相关研究受到广泛关注.本文以2007年为例基于投入产出法研究北京市能源消耗的温室气体排放量,计算得出CH4和N2O这两种常规温室气体排放量.结果表明,北京市2007年能源消耗温室气体排放量为3531.72万tCO2当量,其中CO2排放量为3514.40万t,CH4排放量为1734.32t,N2O排放量为435.83t.北京市工业部门仍然是主要的温室气体排放部门,其排放的温室气体占CO2总量的98.96%,CH4总量的88.48%和N2O总量的98.99%.不同最终使用部门中,政府部门消费产生的温室气体排放量超过总量的15%,高于城镇消费和农村消费之和;调出和出口部门的碳排放量超过总量的40%,所占比例最大.贸易中,隐含在调出和出口部门中温室气体排放量是隐含在调入和进口部门的十几倍.北京市不同行业的温室气体排放强度略优于全国水平.降低北京市温室气体排放量可从进一步优化产业结构,发挥科技减排的作用,提高不同产业的能源利用率等方面采取措施.     

长江经济带湖北省人为源VOCs排放清单及变化特征

以人为源挥发性有机物（VOCs）为研究对象,以5类源活动水平数据为基础,采用排放因子法建立了长江经济带湖北省2018年人为源VOCs排放清单,并进一步研究了2009~2018期间工艺过程源VOCs排放特征及变化趋势.结果表明,湖北省2018年人为源VOCs排放总量为6.52×10⁵ t,约占全国总排放量的6.41%;化石燃料固定燃烧源、工艺过程源、溶剂使用源、移动源和废弃物处理源对湖北省的贡献分别为3.26%、76.39%、4.54%、14.72%和1.09%.涉及9个行业45个子类的工艺过程源在VOCs排放中占比突出,其中武汉市和宜昌市的VOCs排放量较高.从经济水平和区域面积分别分析了各市州工艺过程源VOCs排放强度,天门市和神农架林区单位GDP的VOCs排放量较高,而武汉市、鄂州市和天门市单位面积VOCs排放量较高.2009年VOCs排放量从2.45×10⁵ t逐年递增,2015~2017年趋于稳定,最大排放量达7.01×10⁵ t,2018年VOCs排放量降至4.98×10⁵ t,与全国人为源VOCs排放趋势相同.化学原料及化学制品制造业、橡胶和塑料制造业是其变化的主要驱动力,10年间贡献分别为33.85%~51.55%和7.07%~38.13%.其中化学药品原药、化学农药原药生产在10年间对VOCs排放贡献占据重要优势,而泡沫塑料生产排放量变化大,在2015~2017年突增到2.00×10⁴ t以上.湖北省在国家及地方相关政策引导下,重点行业VOCs减排效果显著.     

兰-白城市群主要大气污染物网格化排放清单及来源贡献

甘肃兰-白城市群为我国西北地区重要的重工业基地,大气污染物排放总量较大.研究高空间分辨率的污染物排放清单对于区域空气质量预报预警、减排方案模拟研究及大气污染防治等具有重要的科学意义.本文以兰州和白银为主要研究区域,基于研究区域污染源排放及统计年鉴等数据资料,建立了兰（2015年）-白（2016年）城市群7种（类）主要大气污染物网格化排放清单,并对其空间排放特征以及排放源贡献进行了详尽地讨论分析.结果表明,兰-白城市群7种主要污染物年排放量分别为：NO_x 2.22×10⁵ t、NH₃ 4.53×10⁴ t、VOCs 7.74×10⁴ t、CO 5.62×10⁵ t、PM₁₀ 4.95×10⁵ t、PM_2.5 1.91×10⁵ t和SO₂ 1.37×10⁵ t.其中CO的排放量最大,NH₃的排放量最小.本清单与北大和清华MEIC清单对比结果表明,交通源排放3个清单一致性较高,CO排放总量和其工业源排放与北大和清华MEIC清单排放源相差30%~40%,推测原因主要为清单计算过程中排放因子、分辨率和数据年份的差异.本清单网格化空间分布显示除NH₃外的其他6种（类）污染物,排放主要集中在市区,排放源中工业非燃烧过程源均为最大贡献占比,NH₃的主要贡献源是氮肥的施用及禽畜排放,其污染分布受耕地分布等因素影响较大.因此,减少工业非燃烧过程源、整合优质高效电力供应、使用清洁能源、严格控制工地扬尘、工业粉尘和做好城区绿化等,能有效地降低兰-白城市群NO_x、VOCs、CO、PM₁₀、PM_2.5和SO₂这6种（类）主要污染物的排放.NH₃的减排则主要可从控制氮肥的使用及减少禽畜排放两方面考虑.本研究还利用蒙特卡洛法分析了排放清单的不确定性,NH₃的不确定性最大为-31%~30%,CO的不确定性最小为-18%~16%,清单整体可信度较高.     

浙江临安大气本底站CO浓度及变化特征

利用基于光腔衰荡光谱(CRDS)技术自组装的大气CO在线观测系统,于2010年9月~2012年2月在浙江省临安大气本底站对大气CO进行了在线观测.结果表明临安站四季CO日变化明显受人为活动影响,分别在每日07:00~10:00和19:00~20:00出现峰值,夏季CO日平均浓度和振幅均最低,分别为314.3×10-9±7.6×10-9(摩尔分数,下同)和50.1×10-9±47.9×10-9.该站全年大气CO浓度呈现冬春季高、夏季低的趋势,与北半球瑞士Jungfraujoch站、青海瓦里关等站基本一致,但平均浓度明显高于其他国际站点,全年CO月均值振幅约为286.8×10-9±19.2×10-9.后向轨迹聚类和地面风结果分析表明,临安站非本底CO浓度主要来自于N-NNE-ENE扇区内城市及工业等人为排放所引起.春、夏和冬季最大的浓度抬升均出现在ENE风向,冬季抬升值最大,约为106.3×10-9±58.0×10-9.     

基于双层规划模型的城市固体废物管理系统优化研究

采用变权重组合模型和情景分析法,对北京市的城市固体废物(MSW)产量进行有效地预测.预测结果表明,北京市垃圾产量增幅不大但处理设施容量存在缺口.在科学预测的基础上,构建以温室气体(GHG)控制为上层目标,系统成本最小化为下层目标的双层规划模型(BLPMGMC).该模型的结果表明,焚烧和堆肥将是北京市MSW的主要处理方式;在3个规划时段内,GHG总排放量达到1.67×106t(以CO2当量计),填埋场的CH4和焚烧厂的N2O是GHG排放的主要贡献者;系统总成本达到7.0×109元,其中,65%的成本来自于焚烧厂和堆肥厂.4种模型结果的对比分析表明,双层规划能提供综合经济和环境因素的管理方案.     

基于Urban-RAM模型的上海居民生活碳排放研究

随着全球对碳排放相关研究的不断深入,居民生活引起的能源消耗和碳排放问题引起了研究人员越来越多的关注,但目前鲜有对上海市居民生活整体碳排放的系统研究.本文以2010年为基准年,引入美国劳伦斯伯克利国家实验室开发的Urban-RAM模型,对上海市居民生活碳排放情况进行定量分析,旨在初步掌握上海市居民生活碳排放的总体规模和结构特征,为上海市低碳城市建设和相关决策提供科学依据.研究结果表明,上海市2010年居民生活碳排放总量(CO2e)为4985.7万t,主要以间接排放为主,间接碳排放和直接碳排放分别占居民生活碳排放总量的64.1%和35.9%;上海市居民生活碳排放在各个消费领域的分布不均,直接碳排放主要来自公共和居住建筑领域,该领域的直接碳排量为1065.0万t,占全市居民生活直接碳排放总量的59.5%;间接碳排放主要来自家庭消费领域,该领域的间接碳排量为1625.2万t,占全市居民生活间接碳排放总量的50.9%,其中以食品消费和衣装消费的贡献最大,分别占家庭消费领域碳排放总量的53.5%和29.5%;综合来看,公共和居住建筑领域的整体碳排量最大,为2231.6万t,占全市居民生活碳排放总量的44.8%.     

Greenhouse gas balances of molasses based ethanol in Nepal

This paper evaluates life cycle greenhouse gas (GHG) balances in production and use of molasses-based ethanol (EtOH) in Nepal. The total life cycle emissions of EtOH is estimated at 432.5 kgCO_2eq m⁻³ ethanol (i.e. 20.4 gCO_2eq MJ⁻¹). Avoided emissions are 76.6% when conventional gasoline is replaced by molasses derived ethanol. A sensitivity analysis was performed to verify the impact of variations in material and energy flows, and allocation ratios in the GHG balances. Market prices of sugar and molasses, amount of nitrogen-fertilizers used in sugarcane production, and sugarcane yield per hectare turn out to be important parameters for the GHG balances estimation. Sales of the surplus electricity derived from bagasse could reduce emissions by replacing electricity produced in diesel power plants. Scenario analysis on two wastewater processes for treatment of effluents obtained from ethanol conversion has also been carried out. If wastewater generated from ethanol conversion unit is treated in pond stabilization (PS) treatment process, GHG emissions alarmingly increase to a level of 4032 kgCO_2eq m⁻³ ethanol. Results also show that the anaerobic digestion process (ADP) and biogas recovery without leakages can significantly avoid GHG emissions, and improve the overall emissions balance of EtOH in Nepal. At a 10% biogas leakage, life cycle emissions is 1038 kgCO_2eq m⁻³ ethanol which corresponds to 44% avoided emissions compared to gasoline. On the other hand, total emissions surpass the level of its counterpart (i.e. gasoline) when the leakage of biogas exceeds 23.4%.     

北京市燃煤源排放控制措施的污染物减排效益评估

为分析北京市燃煤源排放控制措施的污染物减排效益,基于MEIC（中国多尺度排放清单模型）,采用情景分析法,评估了北京市电厂能源清洁化与末端治理、燃煤锅炉改造和城区平房区居民采暖改造等措施的污染物减排效益.结果表明,相对于无控情景,2013年北京市电厂能源清洁化与末端治理减少PM_2.5、PM₁₀、SO₂和NO_x排放量为1.28×104、2.10×104、5.13×104和4.98×104 t,分别占无控情景的85%、86%、87%、74%;北京市燃煤锅炉改造减少PM_2.5、PM₁₀、SO₂、NO_x排放量为1.09×104、2.68×104、11.64×104和5.81×104 t,分别占无煤改气情景的83%、89%、83%、83%;北京市老旧平房区的居民采暖改造减少PM_2.5、PM₁₀、SO₂和NO_x排放量分别为630、870、2 070和790 t,均占无煤改电情景的8%.研究显示,北京市从1998年开始采取的各种减排措施有效地减少了污染物的排放,对北京市空气质量改善具有重要意义.      

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (− 0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

基于投入产出生命周期模型的建筑业能耗及敏感性分析

建设活动消耗了大量能源,研究建筑业能耗测算方法并分析其影响因素有助于建筑业的节能减排. 采用投入产出生命周期评价模型测算建筑业能耗,分析建筑业能耗对部门间联系、部门能源强度和建筑业规模等因素变化的敏感性,提出部门对建筑业能耗的技术责任系数和结构责任系数,以判断部门在降低建筑业能耗中的责任. 结果表明,2010年我国建筑业能耗为1.07×109 t(以标准煤计);技术责任系数最大的部门是黑色金属冶炼及延压(0.489 8),其次是非金属矿物制品(0.479 8)和化工(0.211 8)等;除建筑业外,结构责任系数较大的部门包括非金属矿物制品(0.276 4)和黑色金属冶炼及延压(0.246 0)等;建筑业能耗对黑色金属冶炼及延压(0.243 8)和非金属矿物制品(0.203 4)等部门能源强度的变化最敏感. 降低建筑业能耗应注重促进高能耗建材生产节能、强化建设活动科学管理、推广高性能低能耗材料应用、提高基础设施建设水平和深化住房市场化改革等方面.      

Contribution of different sectors to developed countries’ fulfillment of GHG emission reduction targets under the first commitment period of the Kyoto Protocol

Greenhouse gas (GHG) data submitted in April 2014 on land use, land use change and forestry (LULUCF), energy, industrial processes, solvents and other product use, agriculture, and waste for 37 developed countries was analyzed to estimate the relative contributions of different sectors to GHG emission reductions. This GHG data from the first commitment period of the Kyoto Protocol included 35 parties to Annex B of the Kyoto Protocol, the United States and Canada. Results show that the contribution of each sector was, in order: energy (36.9%), industrial processes (12.4%), agriculture (9.9%), LULUCF (7.7%), waste (3.4%), and solvents and other product use (0.1%). The average proportion of base year emissions reduced in each sector by countries in Annex B was, in order: energy (7.4%), agriculture (2.7%), LULUCF (1.9%), industrial processes (1.2%), waste (0.5%), and solvents and other product use (0.1%). Overall, the energy sector contributed the highest GHG emission reductions, while the agriculture and LULUCF sectors also made contributions. Most countries achieved limited absolute GHG reductions from their chosen LULUCF activities, but the relative contribution of GHG emission reductions from LULUCF was significant but small. This suggests that, unless there are substantial changes to accounting rules, future emission reductions will mainly result from mitigation actions targeting fossil fuel consumption, while the agriculture and LULUCF sectors will continue to play auxiliary roles.